Hasil Pencarian  ::  Simpan CSV :: Kembali

"Metode sequential kriging merupakan salah satu metode penaksiran pada data spasial, yang memodifikasi metode kriging yang sudah ada seperti simple kriging, ordinary kriging, dan universal kriging. Modifikasi dilakukan dengan menerapkan suatu sequential linear estimator. Metode ini menggunakan data spasial sama seperti metode kriging lainnya. Pada tugas akhir ini, metode kriging yang dimodifikasi adalah metode simple kriging. Sequential kriging dapat menyelesaikan kesukaran numerik yang berhubungan dengan interpolasi untuk ukuran data besar. Data dibagi dalam beberapa subset dengan tiap subset memungkinkan hanya memiliki satu data saja. Ketika tersedia suatu data tambahan, estimator sequential kriging memperbaiki estimasi sebelumnya dengan menggunakan bobot linier dari selisih antara data yang baru dan estimasi data tambahan dengan menggunakan estimator sebelumnya. Secara teori, dapat ditunjukkan bahwa estimator sequential kriging menghasilkan estimasi yang sama dengan simple kriging. Begitu juga variansi sequential kriging akan sama dengan variansi simple kriging."

"This unique textbook/​reference presents unified coverage of bioinformatics topics relating to both biological sequences and biological networks, providing an in-depth analysis of cutting-edge distributed algorithms, as well as of relevant sequential algorithms. In addition to introducing the latest algorithms in this area, more than fifteen new distributed algorithms are also proposed. Topics and features: Reviews a range of open challenges in biological sequences and networks, beginning with an informal description of the problem before defining it formally Describes in detail both sequential and parallel/​distributed algorithms for each problem, briefly discussing software packages if there are any available Suggests approaches for distributed algorithms as possible extensions to sequential algorithms, when the distributed algorithms for the topic are scarce Proposes a number of new distributed algorithms in each chapter, to serve as potential starting points for further research Concludes each chapter with self-test exercises, a summary of the key points, a comparison of the algorithms described, and a literature review This clearly-written and easy to follow work is ideal as a textbook for graduate and senior undergraduate students of computer science and biology, and as a self-study guide for any interested reader with a basic background in discrete mathematic s and algorithms. Researchers in bioinformatics will also find the book to be a useful reference on this subject. Dr. K. Erciyes is Rector of Izmir University, Turkey, where he also serves as a professor in the Computer Engineering Department. His other publications include the Springer title Distributed Graph Algorithms for Computer Networks."

"An exploration of the interrelated fields of design of experiments and sequential analysis with emphasis on the nature of theoretical statistics and how this relates to the philosophy and practice of statistics."